Modified YOLO Model for Small Platform Application Using SimRepCSP Module with Case Study: Objects Detection Model on GlobalWheat2020 Dataset

In this research, the optional backbone for YOLO model, called SimRepCSP (Simple Re-parameterization Cross Stage Partial module) was proposed, the model aims to address the goals of reducing training and application costs while enhancing model performance on specific dataset. SimRepCSP is constructed by combining Convolution modules and Re-parameterization Convolution (RepConv) modules, similar to YOLOv7, with the Cross Stage Partial (CSP) network serving as the connection between modules. By incorporating SimRepCSP into YOLOv8 as an alternative backbone, the research aims to achieve improvements in both training efficiency and model performance compared to the original backbone. The experiments were conducted using the GlobalWheat2020 dataset, and the results demonstrate a reduction in training and application costs, as well as higher performance metrics compared to YOLOv8 with the original backbone.